Brain MR Image Synthesis with Multi-contrast Self-attention GAN
Summary: arXiv:2604.00070v1 Announce Type: cross
Accurate and complete multi-modal Magnetic Resonance Imaging (MRI) is crucial for neuro-oncological assessment. Each MRI contrast, such as T1c, T1n, T2, and T2f, provides complementary anatomical and pathological information that is vital for thorough tumor evaluation. However, acquiring all modalities for every patient can be impractical due to constraints related to time, cost, and patient comfort. This limitation can restrict comprehensive assessments of tumors.
Introducing 3D-MC-SAGAN
The research proposes a novel framework known as 3D-MC-SAGAN (3D Multi-Contrast Self-Attention Generative Adversarial Network). This unified 3D multi-contrast synthesis framework is designed to generate high-fidelity missing MRI modalities from a single T2 input while explicitly preserving tumor characteristics.
Key Features of the 3D-MC-SAGAN Framework
- Multi-scale 3D Encoder-Decoder Generator: The framework employs a multi-scale generator that incorporates residual connections to improve efficiency and output quality.
- Memory-Bounded Hybrid Attention (MBHA) Block: This innovative component captures long-range dependencies effectively, enhancing the model’s ability to generate coherent and contextually relevant images.
- WGAN-GP Critic: The model is trained using a Wasserstein GAN with Gradient Penalty, which helps improve the stability and quality of the generated images.
- Auxiliary Contrast-Conditioning Branch: This aspect allows the model to produce T2f, T1n, and T1c volumes within a single unified network, simplifying the generative process.
- Segmentation-Consistency Constraint: A frozen 3D U-Net-based segmentation module ensures that the morphology of lesions is preserved throughout the synthesis process.
Comprehensive Objective Function
The composite objective of the model integrates several loss functions to ensure high-quality outputs:
- Adversarial Loss
- Reconstruction Loss
- Perceptual Loss
- Structural Similarity Loss
- Contrast-Classification Loss
- Segmentation-Guided Loss
This multifaceted approach aligns global realism with tumor-preserving structures, ensuring that the generated images not only look realistic but also maintain clinically relevant information.
Performance Evaluation
Extensive evaluations conducted on 3D brain MRI datasets reveal that 3D-MC-SAGAN achieves state-of-the-art quantitative performance. The model generates visually coherent and anatomically plausible contrasts while improving distribution-level realism. Moreover, it maintains tumor segmentation accuracy comparable to fully acquired multi-modal inputs, underscoring its potential to alleviate acquisition burdens without sacrificing clinically meaningful information.
Conclusion
The 3D-MC-SAGAN framework represents a significant advancement in the field of MRI synthesis, offering a promising solution to the challenges of multi-modal MRI acquisition. By providing a method to generate high-quality missing modalities from a single input, this research could enhance neuro-oncological assessments and improve patient outcomes.